cd ../..

/code

%run "source/config/notebook_settings.py"
import os
import mlflow
from mlflow.tracking import MlflowClient
from helpsk.utility import read_pickle
import helpsk as hlp

from source.library.utilities import Timer, log_info, get_config

config = get_config()
mlflow_uri = config['MLFLOW']['URI']
log_info(f"MLFlow URI: {mlflow_uri}")

client = MlflowClient(tracking_uri=mlflow_uri)

2022-06-20 02:29:10 - INFO     | MLFlow URI: http://mlflow_server:1235

Get Latest Experiment Run from MLFlow

# Get the production model version and actual model
production_model_info = client.get_latest_versions(name=config['MLFLOW']['MODEL_NAME'], stages=['Production'])
assert len(production_model_info) == 1
production_model_info = production_model_info[0]
production_model = read_pickle(client.download_artifacts(
    run_id=production_model_info.run_id,
    path='model/model.pkl'
))
log_info(f"Production Model Version: {production_model_info.version}")

2022-06-20 02:29:10 - INFO     | Production Model Version: 2

# get experiment and latest run info
credit_experiment = client.get_experiment_by_name(name=config['MLFLOW']['EXPERIMENT_NAME'])
runs = client.list_run_infos(experiment_id=credit_experiment.experiment_id)
latest_run = runs[np.argmax([x.start_time for x in runs])]

yaml_path = client.download_artifacts(run_id=latest_run.run_id, path='experiment.yaml')
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = yaml_path)

# get the best estimator from the BayesSearchCV
best_estimator = read_pickle(client.download_artifacts(
    run_id=latest_run.run_id,
    path='model/model.pkl'
))

best_estimator.model

Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer(strategy='median'))),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credi...
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker'])])),
                ('model',
                 ExtraTreesClassifier(bootstrap=True, criterion='entropy',
                                      max_depth=99,
                                      max_features=0.031837350792579364,
                                      max_samples=0.9248344222191298,
                                      min_samples_leaf=4, min_samples_split=16,
                                      n_estimators=1235, random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Training & Test Data Info

client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl')

'/code/mlflow-artifact-root/1/7bdbdabe0cfb4219a3ca8b682166db0a/artifacts/x_train.pkl'

with Timer("Loading training/test datasets"):
    X_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl'))
    X_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_test.pkl'))
    y_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_train.pkl'))
    y_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_test.pkl'))

2022-06-20 02:29:11 - INFO     | Timer Started: Loading training/test datasets
2022-06-20 02:29:11 - INFO     | Timer Finished: (0.02 seconds)

log_info(X_train.shape)
log_info(len(y_train))

log_info(X_test.shape)
log_info(len(y_test))

2022-06-20 02:29:11 - INFO     | (800, 20)
2022-06-20 02:29:11 - INFO     | 800
2022-06-20 02:29:11 - INFO     | (200, 20)
2022-06-20 02:29:11 - INFO     | 200

np.unique(y_train, return_counts=True)

(array([0, 1]), array([559, 241]))

np.unique(y_train, return_counts=True)[1] / np.sum(np.unique(y_train, return_counts=True)[1])

array([0.69875, 0.30125])

np.unique(y_test, return_counts=True)[1] / np.sum(np.unique(y_test, return_counts=True)[1])

array([0.705, 0.295])

Cross Validation Results

Best Scores/Params

log_info(f"Best Score: {results.best_score}")

2022-06-20 02:29:11 - INFO     | Best Score: 0.7700760706900486

log_info(f"Best Params: {results.best_params}")

2022-06-20 02:29:11 - INFO     | Best Params: {'model': 'ExtraTreesClassifier()', 'max_features': 0.031837350792579364, 'max_depth': 99, 'n_estimators': 1235, 'min_samples_split': 16, 'min_samples_leaf': 4, 'max_samples': 0.9248344222191298, 'criterion': 'entropy', 'imputer': "SimpleImputer(strategy='median')", 'scaler': 'None', 'pca': "PCA('mle')", 'encoder': 'CustomOrdinalEncoder()'}

# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')

	rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder	model_rank
9	1	0.77	0.73	0.81	ExtraTreesClassifier()	NaN	0.03	99.00	1235.00	16.00	4.00	0.92	entropy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()	1.00
3	2	0.76	0.71	0.81	LogisticRegression()	0.01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()	1.00
10	3	0.76	0.70	0.82	RandomForestClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	None	OneHotEncoder()	1.00
16	7	0.75	0.70	0.79	XGBClassifier()	NaN	NaN	5.00	1157.00	NaN	NaN	NaN	NaN	0.02	3.00	0.69	0.50	0.73	0.03	2.91	NaN	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()	1.00
21	16	0.73	0.68	0.78	LGBMClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.61	0.98	NaN	8.89	7.31	243.00	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()	1.00

results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder
1	0.770	0.732	0.808	ExtraTreesClassifier()	<NA>	0.032	99.000	1,235.000	16.000	4.000	0.925	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
2	0.762	0.714	0.810	LogisticRegression()	0.014	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()
3	0.761	0.702	0.820	RandomForestClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
4	0.759	0.712	0.806	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
5	0.757	0.711	0.803	LogisticRegression()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
6	0.749	0.724	0.774	ExtraTreesClassifier()	<NA>	0.563	71.000	1,725.000	49.000	16.000	0.956	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
7	0.747	0.704	0.789	XGBClassifier()	<NA>	<NA>	5.000	1,157.000	<NA>	<NA>	<NA>	<NA>	0.018	3.000	0.694	0.501	0.726	0.033	2.910	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
8	0.747	0.711	0.782	XGBClassifier()	<NA>	<NA>	4.000	1,414.000	<NA>	<NA>	<NA>	<NA>	0.013	1.000	0.841	0.624	0.542	0.784	1.243	<NA>	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
9	0.746	0.698	0.794	ExtraTreesClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
10	0.745	0.703	0.787	RandomForestClassifier()	<NA>	0.757	44.000	745.000	33.000	6.000	0.608	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	CustomOrdinalEncoder()
11	0.742	0.691	0.793	XGBClassifier()	<NA>	<NA>	2.000	1,671.000	<NA>	<NA>	<NA>	<NA>	0.021	2.000	0.657	0.591	0.780	0.026	3.081	<NA>	SimpleImputer(strategy='median')	None	None	OneHotEncoder()
12	0.738	0.727	0.748	ExtraTreesClassifier()	<NA>	0.861	52.000	1,995.000	33.000	19.000	0.651	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	CustomOrdinalEncoder()
13	0.737	0.716	0.757	RandomForestClassifier()	<NA>	0.528	70.000	1,003.000	37.000	19.000	0.530	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
14	0.734	0.709	0.758	RandomForestClassifier()	<NA>	0.422	68.000	1,348.000	4.000	39.000	0.859	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
15	0.733	0.705	0.760	ExtraTreesClassifier()	<NA>	0.666	19.000	1,346.000	6.000	44.000	0.852	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
16	0.732	0.682	0.782	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.612	0.979	<NA>	8.891	7.306	243.000	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()
17	0.731	0.689	0.772	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.338	0.617	<NA>	10.572	26.393	255.000	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
18	0.729	0.645	0.814	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	StandardScaler()	PCA('mle')	OneHotEncoder()
19	0.727	0.694	0.759	RandomForestClassifier()	<NA>	0.940	40.000	899.000	3.000	16.000	0.696	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
20	0.724	0.685	0.762	XGBClassifier()	<NA>	<NA>	13.000	1,222.000	<NA>	<NA>	<NA>	<NA>	0.080	5.000	0.825	0.700	0.977	0.020	1.105	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
21	0.723	0.705	0.741	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
22	0.719	0.628	0.810	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
23	0.718	0.682	0.753	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.925	0.859	<NA>	14.754	33.903	400.000	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
24	0.714	0.648	0.779	XGBClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
25	0.702	0.683	0.721	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.374	0.990	<NA>	16.223	18.789	378.000	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	imputer	pca	encoder
1	0.761	0.702	0.820	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	OneHotEncoder()
2	0.745	0.703	0.787	0.757	44.000	745.000	33.000	6.000	0.608	gini	SimpleImputer()	None	CustomOrdinalEncoder()
3	0.737	0.716	0.757	0.528	70.000	1,003.000	37.000	19.000	0.530	gini	SimpleImputer(strategy='median')	PCA('mle')	CustomOrdinalEncoder()
4	0.734	0.709	0.758	0.422	68.000	1,348.000	4.000	39.000	0.859	entropy	SimpleImputer(strategy='median')	PCA('mle')	CustomOrdinalEncoder()
5	0.727	0.694	0.759	0.940	40.000	899.000	3.000	16.000	0.696	entropy	SimpleImputer(strategy='median')	None	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	C	imputer	scaler	pca	encoder
1	0.762	0.714	0.810	0.014	SimpleImputer()	StandardScaler()	PCA('mle')	OneHotEncoder()
2	0.759	0.712	0.806	0.000	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
3	0.757	0.711	0.803	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
4	0.729	0.645	0.814	0.000	SimpleImputer(strategy='most_frequent')	StandardScaler()	PCA('mle')	OneHotEncoder()
5	0.723	0.705	0.741	0.000	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()

BayesSearchCV Performance Over Time

results.plot_performance_across_trials(facet_by='model').show()

results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

---

Variable Performance Over Time

results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

---

Scatter Matrix

# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

---

Variable Performance - Numeric

results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)

results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

---

Variable Performance - Non-Numeric

results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

---

results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)

---

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

---

Best Model - Test Set Performance

test_predictions = best_estimator.predict(X_test)
test_predictions[0:10]

array([0.32794716, 0.32893009, 0.37430263, 0.3052219 , 0.23870015,
       0.31131838, 0.24759746, 0.33864031, 0.23092148, 0.24922654])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.781	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.169	0.000	1.000	16.9% of positive instances were correctly identified.; i.e. 10 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.957	1.000	0.000	95.7% of negative instances were correctly identified.; i.e. 135 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.043	0.000	1.000	4.3% of negative instances were incorrectly identified as positive; i.e. 6 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.831	1.000	0.000	83.1% of positive instances were incorrectly identified as negative; i.e. 49 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.625	0.000	0.295	When the model claims an instance is positive, it is correct 62.5% of the time; i.e. out of the 16 times the model predicted "Positive Class", it was correct 10 times; a.k.a precision
Negative Predictive Value	0.734	0.705	0.000	When the model claims an instance is negative, it is correct 73.4% of the time; i.e. out of the 184 times the model predicted "Negative Class", it was correct 135 times
F1 Score	0.267	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.621	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.725	0.705	0.295	72.5% of instances were correctly identified
Error Rate	0.275	0.295	0.705	27.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.17	3.39
10	0.20	2.03
15	0.34	2.26
20	0.44	2.20
25	0.49	1.97
30	0.56	1.86
35	0.63	1.79
40	0.69	1.74
45	0.76	1.69
50	0.80	1.59
55	0.83	1.51
60	0.85	1.41
65	0.86	1.33
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---

Production Model - Test Set Performance

test_predictions = production_model.predict(X_test)
test_predictions[0:10]

array([0.32794716, 0.32893009, 0.37430263, 0.3052219 , 0.23870015,
       0.31131838, 0.24759746, 0.33864031, 0.23092148, 0.24922654])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.781	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.169	0.000	1.000	16.9% of positive instances were correctly identified.; i.e. 10 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.957	1.000	0.000	95.7% of negative instances were correctly identified.; i.e. 135 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.043	0.000	1.000	4.3% of negative instances were incorrectly identified as positive; i.e. 6 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.831	1.000	0.000	83.1% of positive instances were incorrectly identified as negative; i.e. 49 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.625	0.000	0.295	When the model claims an instance is positive, it is correct 62.5% of the time; i.e. out of the 16 times the model predicted "Positive Class", it was correct 10 times; a.k.a precision
Negative Predictive Value	0.734	0.705	0.000	When the model claims an instance is negative, it is correct 73.4% of the time; i.e. out of the 184 times the model predicted "Negative Class", it was correct 135 times
F1 Score	0.267	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.621	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.725	0.705	0.295	72.5% of instances were correctly identified
Error Rate	0.275	0.295	0.705	27.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.17	3.39
10	0.20	2.03
15	0.34	2.26
20	0.44	2.20
25	0.49	1.97
30	0.56	1.86
35	0.63	1.79
40	0.69	1.74
45	0.76	1.69
50	0.80	1.59
55	0.83	1.51
60	0.85	1.41
65	0.86	1.33
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

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